Summary: 1
Experimental Evaluation of Performance Improvements
in Abductive Network Classifiers with Problem Decomposition
R. E. Abdel-Aal
Center for Applied Physical Sciences, Research Institute,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Abstract:
Problem decomposition and divide-and-conquer strategies have been proposed to improve the
performance and realization of neural network solutions for complex problems. This paper reports
on an experimental evaluation of performance gains brought about by problem decomposition for
abductive network classifiers that classify four noisy waveform patterns having two waveform
types (sine/cosine) and two different frequencies. Two-stage problem decomposition improves
overall classification accuracy from 87.2% to 99%. Problem decomposition classifiers were found
to be much more tolerant to model simplification and reduction in the training set size compared to
monolithic solutions. This allows trading off some of the large gain in classification performance
for some other advantages that may be quite desirable in some applications, such as simpler models
that execute faster and are easier to implement, smaller training sets, and shorter training times. A
problem decomposition classifier is more accurate than a monolithic classifier in spite of the former
being five times simpler, executing over two times faster, requiring one fifth of the training data,
and synthesized in one eleventh of the training time. Performance is comparable with a neural